Visual predictive architecture for biologically inspired object recognition

As the quantity of visual information sources increases, the need to develop sensors that can automatically alert the user to exceptional events is being emphasized. This feature incorporates the ability to detect and classify the targets they “see”. Achieving this goal will dramatically improve the efficiency of CCTV-based security systems, improve search/retrieval engines, increase the autonomy of robotic systems and will contribute in many other areas of life. The performance of the human visual system and its robustness to image degradations still surpasses the best computer vision systems. Remarkable in particular, is the human brain high accuracy in ultra rapid object categorization tasks. Recent studies shows that the mechanism behind the recognition process includes predictions based on prior knowledge about the world. These predictions enable rapid generation of hypothesis that biases the outcome of the recognition process in situation of uncertainty. In this work, we implemented the concepts behind this top-down prediction mechanism of the human visual system. This work focus on the orbitofrontal cortex (OFC) role in the prediction process, which appears to be attuned to the associative content of visual information and to facilitate recognition of sensory inputs via predictive feedback to sensory cortices. Specifically, coarse representations reach the OFC, which generate “initial guesses” regarding the targets identity. These predictions are projected to the inferior temporal cortex, which facilitate perception and select the most likely interpretations. We show that imitating this mechanism can potentially create more robust target recognition models than exist today.